text: import sys
  import os.path
  import glob
  import cv2
  import numpy as np
  import torch
  import architecture as arch
  
  model_path = sys.argv[1]  # pretrained_models/nESRGANplus.pth pretrained_models/RRDB_ESRGAN_x4.pth OR pretrained_models/RRDB_PSNR_x4.pth
  device = torch.device('cuda')  # if you want to run on CPU, change 'cuda' -> cpu
  # device = torch.device('cpu')
  
  test_img_folder = 'test_image/LR/*'
  
  model = arch.RRDB_Net(3, 3, 64, 23, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', \
                          mode='CNA', res_scale=1, upsample_mode='upconv')
  model.load_state_dict(torch.load(model_path), strict=False)
  model.eval()
  for k, v in model.named_parameters():
      v.requires_grad = False
  model = model.to(device)
  
  print('Model path {:s}. \nTesting...'.format(model_path))
  
  idx = 0
  for path in glob.glob(test_img_folder):
      idx += 1
      base = os.path.splitext(os.path.basename(path))[0]
      print(idx, base)
      # read image
      img = cv2.imread(path, cv2.IMREAD_COLOR)
      img = img * 1.0 / 255
      img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
      img_LR = img.unsqueeze(0)
      img_LR = img_LR.to(device)
  
      output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
      output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
      output = (output * 255.0).round()
      cv2.imwrite('test_image/results/{:s}_rlt.png'.format(base), output)
  